Proliferating mod ID version 2
Jack Collora
2023-06-01
module discovery
The ModID function above is modified to accept a batch variable and to correct with combat. Combat was choosen as opposed to more single cell specific techniques (rPCA, Harmony, FastMNN, etc.) for a few reasons. 1. Low cell numbers - we don’t have enough different cells for our conditions for graph based methods to learn things efficiently. 2. Need corrected gene values - the above correct in a reduced PCA like space, not the gene expression values themselves. CCA is an alternative here but the concern from point 1 remains. 3. Reduce chance of overcorrection. Combat’s weakness in most applications is that it corrects only weekly and this is not sufficient to overcome tech or batch effects when applying unsupervised methods across those variables. It does not (at least in the studies I’ve seen) force disparate cell types together in the way that CCA can, which otherwise makes the DE testing have elevated false discovery.
results<-subset(results, idents = "Proliferating")## Loading required package: Signacresults$batch<-case_when(grepl("4|5|6",results$orig.ident)~1,grepl("7|8|9",results$orig.ident)~2,grepl("10|11|12",results$orig.ident)~3 )
mods<-ModID(results, combat=TRUE, batch=results$batch, nPCS = 8, softpower = 16)## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at -2.4183## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 0.30103## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 9.8548e-16## PC_ 1
## Positive: ANK3, TSHZ2, ZBTB20, FAAH2, MAML2, PRKCA, LDLRAD4, PCAT1, ABLIM1, TTN
## MGAT4A, C22orf34, UST, AL136456.1, PFKFB3, TAFA1, SESN3, MPP7, LINC01422, AL353660.1
## Negative: TUBA1B, TUBB, HIST1H4C, GAPDH, RRM2, STMN1, DIAPH3, ACTB, PFN1, HIST1H3B
## POLQ, TYMS, NCAPG, TK1, HIST1H2AH, TMPO, PCLAF, HIST1H1B, CFL1, HMGN2
## PC_ 2
## Positive: POLQ, NCAPG, DIAPH3, SPC25, CIT, KIF18B, KIF15, RRM2, ASPM, ATAD5
## NCAPG2, CDCA2, NUSAP1, KIF11, KIF14, TOP2A, KNL1, BUB1B, KIF4A, BRIP1
## Negative: RPL28, RPL18A, RPL41, RPS15, RPL27A, RPL13A, RPS28, RPS19, RPL11, RPS12
## RPS23, RPS29, RPS18, RPS27, RPL10, RPL18, RPL37, RPS16, B2M, RPLP1
## PC_ 3
## Positive: ABLIM1, ZBTB20, MGAT5, MAP3K1, RORA, WDFY2, SSBP2, MAP3K4, RFX3, MCF2L2
## TSHZ2, ARL15, AL136456.1, DUSP16, AKT3, ATP13A3, PHF21A, UST, MAST4, PRKCA
## Negative: MTRNR2L8, NKG7, AGAP1, PECAM1, CCL5, GZMA, MKI67, CD8A, CENPF, HMGB2
## KLRK1, CD8B, AOAH, ZEB2, KPNA2, NAALADL2, TOP2A, HIST1H1E, HSP90B1, INCENP
## PC_ 4
## Positive: PTPN3, MIR4422HG, PECAM1, DTHD1, LYST, SESTD1, GZMK, NCOA1, MCTP2, CNR2
## HAVCR2, AL031599.1, UBE2E2, AGAP1, AFF3, KLRK1, ANTXR2, TRPS1, SAMD3, RPTOR
## Negative: LTB, SORL1, MAP3K1, ANK3, TTC39C, KIF14, TSHZ2, CDCA2, KIF4A, CKAP2L
## KIF2C, TNFAIP3, CDCA8, JUNB, UBE2C, ESPL1, RPS19, EPHA4, FMN1, SGO2
## PC_ 5
## Positive: DTL, MSH6, CLSPN, TRERF1, EXO1, CDC45, FANCA, MCM4, CDC6, CCNE2
## CTSW, HELLS, CCL5, ZEB2, POLE2, MCOLN2, C1orf21, MTHFD1L, FAM111B, THEMIS
## Negative: SESN3, SESTD1, MIR4422HG, KIF14, PTPN3, LDLRAD4, PLK1, KIF20A, KIF23, AFF3
## KIF4A, HMMR, BACH2, AURKB, TXK, DTHD1, CCNB2, APOLD1, AURKA, CDCA8
## Found 7 genes with uniform expression within a single batch (all zeros); these will not be adjusted for batch.## Found3batches## Adjusting for0covariate(s) or covariate level(s)## Standardizing Data across genes## Fitting L/S model and finding priors## Finding parametric adjustments## Adjusting the Data## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.0541 2.49 0.3810 157.000 157.0000 176.00
## 2 2 0.1100 -1.56 0.3420 85.300 83.7000 109.00
## 3 3 0.4000 -2.11 0.4900 47.400 45.0000 70.90
## 4 4 0.6220 -2.05 0.6440 27.100 24.4000 48.20
## 5 5 0.6670 -2.04 0.6290 16.000 13.3000 34.10
## 6 6 0.6800 -1.89 0.6630 9.730 7.3000 24.90
## 7 7 0.6400 -1.79 0.6040 6.150 4.1000 18.70
## 8 8 0.2200 -3.92 0.1170 4.020 2.2900 14.40
## 9 9 0.2210 -3.47 0.1360 2.710 1.3100 11.20
## 10 10 0.2240 -3.12 0.1440 1.890 0.8090 8.86
## 11 12 0.7490 -1.37 0.7940 0.983 0.3050 5.72
## 12 14 0.7670 -1.24 0.8080 0.554 0.1240 3.81
## 13 16 0.7710 -1.22 0.8000 0.330 0.0490 2.59
## 14 18 0.1990 -2.04 0.0217 0.204 0.0222 1.80
## 15 20 0.2000 -1.94 0.0272 0.130 0.0091 1.27
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## dynamicMods
## 0 1 2 3 4 5 6
## 212 21 15 14 13 12 11
## mergeCloseModules: Merging modules whose distance is less than 0.5
## multiSetMEs: Calculating module MEs.
## Working on set 1 ...
## moduleEigengenes: Calculating 7 module eigengenes in given set.
## multiSetMEs: Calculating module MEs.
## Working on set 1 ...
## moduleEigengenes: Calculating 5 module eigengenes in given set.
## Calculating new MEs...
## multiSetMEs: Calculating module MEs.
## Working on set 1 ...
## moduleEigengenes: Calculating 5 module eigengenes in given set.
## $yellow
## [1] "PCAT1" "UST" "MPP7" "EPHA4" "SSBP2"
## [6] "AOAH" "SESTD1" "JUNB" "AP000787.1" "NDUFA6"
## [11] "H2AFZ" "PKM" "DISC1"
##
## $red
## [1] "TUBA1B" "TUBB" "DIAPH3" "POLQ" "NCAPG" "PCLAF"
## [7] "HIST1H1B" "PCNA" "ASPM" "CIT" "KIF15" "ATAD5"
## [13] "NCAPG2" "KIF11" "TOP2A" "KNL1" "BRIP1" "CIP2A"
## [19] "BRCA2" "ECT2" "FANCI" "CENPF" "DTL" "CLSPN"
## [25] "FANCA" "HELLS" "HNRNPAB" "CENPP" "C12orf75" "MCM5"
## [31] "MCM7" "KNTC1"
##
## $brown
## [1] "STMN1" "DUSP16" "STAM" "CDHR3" "CCL5" "HSP90B1" "MCTP2"
## [8] "ANTXR2" "APOLD1" "SCMH1" "AGPAT4" "ANXA2" "SYTL2" "TGFBR3"
##
## $blue
## [1] "RPL28" "RPL18A" "RPL41" "RPS15" "RPL27A" "RPL13A" "RPS28" "RPS19"
## [9] "RPL11" "RPS12" "RPS23" "RPS29" "RPS18" "RPS27" "RPL10" "RPL18"
## [17] "RPL37" "RPS16" "B2M" "RPLP1" "RPS2" "RPS27A" "RPL37A" "RPS15A"
## [25] "RPLP2" "TMSB10" "RPS24"
##
## [1] "0/10000 permutations failed the Mann-Whitney test."
## [1] "0/10000 permutations failed the Mann-Whitney test."
## [1] "13/10000 permutations failed the Mann-Whitney test."
## [1] "0/10000 permutations failed the Mann-Whitney test."
## $yellow
## [1] "PCAT1" "UST" "MPP7" "EPHA4" "SSBP2"
## [6] "AOAH" "SESTD1" "JUNB" "AP000787.1" "NDUFA6"
## [11] "H2AFZ" "PKM" "DISC1"
##
## $red
## [1] "TUBA1B" "TUBB" "DIAPH3" "POLQ" "NCAPG" "PCLAF"
## [7] "HIST1H1B" "PCNA" "ASPM" "CIT" "KIF15" "ATAD5"
## [13] "NCAPG2" "KIF11" "TOP2A" "KNL1" "BRIP1" "CIP2A"
## [19] "BRCA2" "ECT2" "FANCI" "CENPF" "DTL" "CLSPN"
## [25] "FANCA" "HELLS" "HNRNPAB" "CENPP" "C12orf75" "MCM5"
## [31] "MCM7" "KNTC1"
##
## $brown
## [1] "STMN1" "DUSP16" "STAM" "CDHR3" "CCL5" "HSP90B1" "MCTP2"
## [8] "ANTXR2" "APOLD1" "SCMH1" "AGPAT4" "ANXA2" "SYTL2" "TGFBR3"
##
## $blue
## [1] "RPL28" "RPL18A" "RPL41" "RPS15" "RPL27A" "RPL13A" "RPS28" "RPS19"
## [9] "RPL11" "RPS12" "RPS23" "RPS29" "RPS18" "RPS27" "RPL10" "RPL18"
## [17] "RPL37" "RPS16" "B2M" "RPLP1" "RPS2" "RPS27A" "RPL37A" "RPS15A"
## [25] "RPLP2" "TMSB10" "RPS24"saveRDS(mods, "~/gibbs/DOGMAMORPH/Ranalysis/modules/20230601ProliferatingModules.rds")
for (i in names(mods)){
write.table(mods[[i]], file=paste0("~/gibbs/DOGMAMORPH/Ranalysis/modules/proliferation/combat_",i,".txt"), quote = FALSE, row.names = FALSE, col.names = FALSE, sep = "")
}annotating modules
After correction, we find 4 modules, of those four none appear paritcularly different across treatment, batch, or timepoint. Individual to individual variation is present, but its ultimately not aligned with an interesting variable.
Further iterations are below either doing the batch effect correction before feature identification or without merging modules based on similarity. In all, it looks like the only two consistent signal (at a module level) in proliferating cells is that: 1. Proliferation 2. Ribosomal/Housekeeping
results<-AddModuleScore(results, mods, name = names(mods))
modnames<-paste0(names(mods), seq(1, length(names(mods))))
FeaturePlot(results, features = modnames, min.cutoff = 'q5', max.cutoff = 'q95')
VlnPlot(results, features = modnames, split.by = "Participant")## The default behaviour of split.by has changed.
## Separate violin plots are now plotted side-by-side.
## To restore the old behaviour of a single split violin,
## set split.plot = TRUE.
##
## This message will be shown once per session.
VlnPlot(results, features = modnames, split.by = "batch")
VlnPlot(results, features = modnames, split.by = "Treatment")
VlnPlot(results, features = modnames, split.by = "Timepoint")
#borrowing some rushmore colors from the wes anderson color pack
BottleRocket2 = c("#FAD510", "#CB2314", "#273046", "#354823", "#1E1E1E")
#defining a quick function to read in and plot Enrichr
PlotEnrichment<-function(file, topn=10, returnplot=TRUE, title="Significant Pathways"){
data<-read.table(file, sep = "\t", header=1)
data$value<- -log10(data$Adjusted.P.value)
#order by most signfiicant
data$Term<-factor(data$Term, levels = rev(data$Term))
data<-data[order(data$value, decreasing = TRUE),]
#subset to top n
data<-data[1:topn,]
plot<-ggplot(data, aes(x=value, y=Term))+geom_bar(stat="identity", fill=BottleRocket2[2])+xlab("-log10(p_adj)")+ylab("Pathway")+ggtitle(title)+theme_classic()
if(returnplot){return(plot)}
return(data)
}
todo<-list.files("~/gibbs/DOGMAMORPH/Ranalysis/modules/proliferation/enrichr_combat/")
for (i in todo){
print(PlotEnrichment(paste0("~/gibbs/DOGMAMORPH/Ranalysis/modules/proliferation/enrichr_combat/",i), topn = 20, title=i))
}
running combat before running module ID
Rational here is that if we correct for batch before doing variable features and PCA we may be able to discover more true signals. In reality, we end up very consistently recovering proliferation and ribosomal modules only despite different npcs and softpowers.
ModID<-function(seuratobj, cluster=NA, prefix="modules", nPCS="", softpower="", combat=FALSE, batch=NA){
#subset to a cluster or clusters of interest if desired
if (sum(!is.na(cluster))>1){test_clus<-subset(seuratobj, idents=cluster)}else{test_clus<-seuratobj}
#perform feature selection, scaling, and PCA
if(combat==TRUE){
test_clus_2<-test_clus@assays$RNA@data
test_clus_2<-ComBat(test_clus_2,batch)
test_clus@assays$RNA@data <- test_clus_2
}
test_clus<-FindVariableFeatures(test_clus, verbose=FALSE)
test_clus<-ScaleData(test_clus, verbose=FALSE)
test_clus<-RunPCA(test_clus, verbose=FALSE)
#prompot for number of PCs needed
if(nPCS==""){print(ElbowPlot(test_clus))}
while(nPCS==""){nPCS<-readline("how many NPCs?")}
nPCS<-as.integer(nPCS)
#expanding the genes by projecting the dims to more PCs
test_clus<-ProjectDim(test_clus)
#gere we'll grab the top 25 features (positive and negative) associated with each dimension
genes<-c()
for (i in 1:nPCS){genelist<-TopFeatures(test_clus, dim = i, nfeatures = 50,balanced = T, projected = T )
for (i in 1:length(genelist)){genes<-c(genes, genelist[[i]])}}
#remove duplicates
genes<-unique(genes)
#grab the matrix from the seurat object
test_clus<-as.matrix(test_clus@assays$RNA@data[genes,])
#prompt for softpower
FindPower(datExpr=t(test_clus))
while(softpower==""){softpower<-readline("what softpower?")}
softpower<-as.integer(softpower)
#run the remaining functions to actually cut the tree
test_clus_tom<-ClusterTOM(datExpr = t(test_clus), softPower = softpower)
test_clus_mods<-CutTOMTree(datExpr = t(test_clus), geneTree = test_clus_tom$geneTree, dissTOM = test_clus_tom$dissTOM, minModuleSize = 10 )
test_clus_merge_mods<-MergeSimilarModules(datExpr=t(test_clus), dynamicColors = test_clus_mods$dyanmicColors, geneTree = test_clus_tom$geneTree, MEDissThres = 0.5)
print(test_clus_merge_mods$merged_modules)
test_clus_merge_mods.isSig = sapply(test_clus_merge_mods$merged_modules, function(module){
TestModuleSignificance(mod = module, dissTOM = test_clus_tom$dissTOM, expr.data = test_clus,
n_perm = 10000, pval = 0.05, n.bin = 10)
})
test_clus_merge_mods.isSig = test_clus_merge_mods$merged_modules[test_clus_merge_mods.isSig]
print(test_clus_merge_mods.isSig)
names(test_clus_merge_mods.isSig)<-paste(prefix,names(test_clus_merge_mods.isSig ), sep="_")
return(test_clus_merge_mods.isSig)
}
#tried npcs/softpower
#8/18 - Ribosomal and a proliferating
#7/18 - Ribosomal and a proliferating
#5/18 - Ribosomal and a proliferating
#8/10 - Ribosomal and a proliferating
#8/5 - Ribosomal and a proliferating
#5/5 - Ribosomal and a proliferating
mods<-ModID(results, combat = TRUE, batch = results$batch, nPCS = 8, softpower = 18)## Found 24380 genes with uniform expression within a single batch (all zeros); these will not be adjusted for batch.## Found3batches## Adjusting for0covariate(s) or covariate level(s)## Standardizing Data across genes## Fitting L/S model and finding priors## Finding parametric adjustments## Adjusting the Data## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at -2.4183## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 0.30103## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 9.8548e-16## PC_ 1
## Positive: ANK3, ZBTB20, PCAT1, FAAH2, TSHZ2, MAML2, ABLIM1, PRKCA, SESN3, TTN
## LDLRAD4, UST, MPP7, FMN1, AL353660.1, ACYP2, TAFA1, MGAT4A, AL136456.1, EPHA4
## Negative: TUBA1B, TUBB, HIST1H4C, RRM2, STMN1, DIAPH3, HIST1H3B, GAPDH, POLQ, ACTB
## HIST1H2AH, NCAPG, TK1, HIST1H1B, PFN1, TMPO, NUSAP1, PCLAF, SHCBP1, ASPM
## PC_ 2
## Positive: POLQ, NCAPG, SPC25, DIAPH3, CIT, KIF15, KIF18B, RRM2, ASPM, KIF11
## CDCA2, NCAPG2, ATAD5, NUSAP1, KIF14, KIF4A, TOP2A, BUB1B, KNL1, BRIP1
## Negative: RPL28, RPL18A, RPL41, RPL13A, RPS15, RPL27A, RPS19, RPL11, RPS12, RPS28
## RPS23, RPS29, RPS18, RPL10, RPL37, RPS27, RPS2, RPLP1, B2M, RPS16
## PC_ 3
## Positive: ABLIM1, MGAT5, RORA, MAP3K1, ZBTB20, SSBP2, WDFY2, MAP3K4, MCF2L2, ATP13A3
## AL136456.1, RFX3, ARL15, TSHZ2, PHF21A, STAM, DUSP16, AKT3, MAST4, UST
## Negative: MTRNR2L8, NKG7, AGAP1, CCL5, PECAM1, CD8A, MKI67, CENPF, GZMA, KLRK1
## HMGB2, TOP2A, CD8B, INCENP, KPNA2, AOAH, HIST1H1E, CENPE, CDCA2, DLGAP5
## PC_ 4
## Positive: PECAM1, PTPN3, DTHD1, MIR4422HG, LYST, MCTP2, NCOA1, GZMK, KLRK1, CNR2
## SESTD1, NKG7, AGAP1, AL031599.1, HAVCR2, KCNQ5, UBE2E2, SAMD3, GZMA, AFF3
## Negative: LTB, MAP3K1, SORL1, KIF14, KIF4A, CDCA2, CDCA8, UBE2C, KIF23, TSHZ2
## ANK3, CKAP2L, KIF2C, PLK1, KIF20A, CCNB2, ESPL1, CCNF, GTSE1, HMMR
## PC_ 5
## Positive: MIR4422HG, PTPN3, SESTD1, DTHD1, AFF3, GZMK, SESN3, AL031599.1, LDLRAD4, PECAM1
## UBE2E2, ANTXR2, BACH2, LYST, HAVCR2, KIF20A, PLK1, DSCAML1, PACSIN1, KIF14
## Negative: EXO1, DTL, FANCA, CLSPN, TRERF1, CCNE2, CDC6, MSH6, CDC45, CTSW
## MCM4, POLE2, FAM111B, HELLS, MCOLN2, CENPP, C1orf21, CCL5, MPP6, MCM10
## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.0259 1.50 0.270 155.000 1.54e+02 173.00
## 2 2 0.1620 -1.85 0.310 83.900 8.23e+01 107.00
## 3 3 0.4210 -2.20 0.444 46.700 4.41e+01 70.00
## 4 4 0.6170 -2.07 0.595 26.800 2.38e+01 47.80
## 5 5 0.6470 -1.92 0.591 15.900 1.29e+01 34.00
## 6 6 0.6510 -1.81 0.615 9.800 7.19e+00 25.00
## 7 7 0.6210 -1.70 0.582 6.250 3.96e+00 18.90
## 8 8 0.6410 -1.54 0.599 4.140 2.28e+00 14.60
## 9 9 0.6930 -1.44 0.669 2.830 1.34e+00 11.50
## 10 10 0.6790 -1.33 0.653 1.990 8.09e-01 9.10
## 11 12 0.2150 -2.62 0.131 1.070 3.02e-01 5.91
## 12 14 0.2100 -2.26 0.143 0.612 1.22e-01 3.96
## 13 16 0.2080 -2.13 0.146 0.370 5.02e-02 2.71
## 14 18 0.8330 -1.12 0.877 0.231 2.27e-02 1.90
## 15 20 0.8060 -1.11 0.840 0.148 9.02e-03 1.35
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## dynamicMods
## 0 1 2 3 4
## 195 35 25 25 13
## mergeCloseModules: Merging modules whose distance is less than 0.5
## multiSetMEs: Calculating module MEs.
## Working on set 1 ...
## moduleEigengenes: Calculating 5 module eigengenes in given set.
## multiSetMEs: Calculating module MEs.
## Working on set 1 ...
## moduleEigengenes: Calculating 4 module eigengenes in given set.
## Calculating new MEs...
## multiSetMEs: Calculating module MEs.
## Working on set 1 ...
## moduleEigengenes: Calculating 4 module eigengenes in given set.
## $brown
## [1] "PCAT1" "FAAH2" "UST" "ACYP2" "EPHA4" "CMTM8"
## [7] "ZNF331" "HIST1H4C" "STAM" "DUSP16" "CDHR3" "CCL5"
## [13] "MCTP2" "SESTD1" "HNRNPAB" "SCMH1" "JUNB" "SYTL2"
## [19] "AGPAT4" "C12orf75" "TENT5C" "PKM" "CD38" "DISC1"
## [25] "ST8SIA1"
##
## $turquoise
## [1] "TUBA1B" "TUBB" "RRM2" "STMN1" "DIAPH3" "POLQ"
## [7] "NCAPG" "HIST1H1B" "NUSAP1" "PCLAF" "ASPM" "KIF11"
## [13] "NCAPG2" "TOP2A" "CIT" "KIF15" "ATAD5" "KNL1"
## [19] "BRIP1" "MKI67" "CIP2A" "FANCI" "STIL" "ECT2"
## [25] "SSBP2" "MCF2L2" "CD226" "CENPF" "CENPE" "AFF3"
## [31] "ANTXR2" "LTB" "APOLD1" "DTL" "FANCA" "CLSPN"
## [37] "MCM4" "HELLS" "CENPP" "WDR76" "FHIT" "ANXA2"
## [43] "TGFBR3" "MCM5" "MCM7" "POLA1" "BRCA2" "KNTC1"
##
## $blue
## [1] "RPL28" "RPL18A" "RPL41" "RPL13A" "RPS15" "RPL27A" "RPS19" "RPL11"
## [9] "RPS12" "RPS28" "RPS23" "RPS29" "RPS18" "RPL10" "RPL37" "RPS2"
## [17] "RPLP1" "B2M" "RPS16" "RPL18" "RPL37A" "RPS27A" "RPLP2" "RPS15A"
## [25] "RPL3"
##
## [1] "7604/10000 permutations failed the Mann-Whitney test."
## [1] "0/10000 permutations failed the Mann-Whitney test."
## [1] "0/10000 permutations failed the Mann-Whitney test."
## $turquoise
## [1] "TUBA1B" "TUBB" "RRM2" "STMN1" "DIAPH3" "POLQ"
## [7] "NCAPG" "HIST1H1B" "NUSAP1" "PCLAF" "ASPM" "KIF11"
## [13] "NCAPG2" "TOP2A" "CIT" "KIF15" "ATAD5" "KNL1"
## [19] "BRIP1" "MKI67" "CIP2A" "FANCI" "STIL" "ECT2"
## [25] "SSBP2" "MCF2L2" "CD226" "CENPF" "CENPE" "AFF3"
## [31] "ANTXR2" "LTB" "APOLD1" "DTL" "FANCA" "CLSPN"
## [37] "MCM4" "HELLS" "CENPP" "WDR76" "FHIT" "ANXA2"
## [43] "TGFBR3" "MCM5" "MCM7" "POLA1" "BRCA2" "KNTC1"
##
## $blue
## [1] "RPL28" "RPL18A" "RPL41" "RPL13A" "RPS15" "RPL27A" "RPS19" "RPL11"
## [9] "RPS12" "RPS28" "RPS23" "RPS29" "RPS18" "RPL10" "RPL37" "RPS2"
## [17] "RPLP1" "B2M" "RPS16" "RPL18" "RPL37A" "RPS27A" "RPLP2" "RPS15A"
## [25] "RPL3"ID modules without merging similar
Rational here is that we might be losing interesting variation by merging. Reality was that we ended up with more modules but they mapped back to the same things.
ModID<-function(seuratobj, cluster=NA, prefix="modules", nPCS="", softpower="", combat=FALSE, batch=NA){
#subset to a cluster or clusters of interest if desired
if (sum(!is.na(cluster))>1){test_clus<-subset(seuratobj, idents=cluster)}else{test_clus<-seuratobj}
#perform feature selection, scaling, and PCA
if(combat==TRUE){
test_clus_2<-test_clus@assays$RNA@data
test_clus_2<-ComBat(test_clus_2,batch)
test_clus@assays$RNA@data <- test_clus_2
}
test_clus<-FindVariableFeatures(test_clus, verbose=FALSE)
test_clus<-ScaleData(test_clus, verbose=FALSE)
test_clus<-RunPCA(test_clus, verbose=FALSE)
#prompot for number of PCs needed
if(nPCS==""){print(ElbowPlot(test_clus))}
while(nPCS==""){nPCS<-readline("how many NPCs?")}
nPCS<-as.integer(nPCS)
#expanding the genes by projecting the dims to more PCs
test_clus<-ProjectDim(test_clus)
#gere we'll grab the top 25 features (positive and negative) associated with each dimension
genes<-c()
for (i in 1:nPCS){genelist<-TopFeatures(test_clus, dim = i, nfeatures = 50,balanced = T, projected = T )
for (i in 1:length(genelist)){genes<-c(genes, genelist[[i]])}}
#remove duplicates
genes<-unique(genes)
#grab the matrix from the seurat object
test_clus<-as.matrix(test_clus@assays$RNA@data[genes,])
#prompt for softpower
FindPower(datExpr=t(test_clus))
while(softpower==""){softpower<-readline("what softpower?")}
softpower<-as.integer(softpower)
#run the remaining functions to actually cut the tree
test_clus_tom<-ClusterTOM(datExpr = t(test_clus), softPower = softpower)
test_clus_mods<-CutTOMTree(datExpr = t(test_clus), geneTree = test_clus_tom$geneTree, dissTOM = test_clus_tom$dissTOM, minModuleSize = 10 )
#omiting those that will merge modules so that we can test more individual modules to possibly discover more nuanced differences
test_clus_merge_mods.isSig = sapply(test_clus_mods$modules, function(module){
TestModuleSignificance(mod = module, dissTOM = test_clus_tom$dissTOM, expr.data = test_clus,
n_perm = 10000, pval = 0.05, n.bin = 10)
})
test_clus_merge_mods.isSig = test_clus_mods$modules[test_clus_merge_mods.isSig]
print(test_clus_merge_mods.isSig)
names(test_clus_merge_mods.isSig)<-paste(prefix,names(test_clus_merge_mods.isSig ), sep="_")
return(test_clus_merge_mods.isSig)
}
#tried npcs/softpower
#7/18 - 3 modules identified - proliferation, ribosomal, and second proliferation
#8/10 - 5 modules, ribosomal + 4 proliferating
#5/5 - 3 modules identified - proliferation, ribosomal, and second proliferation
mods<-ModID(results, combat = TRUE, batch = results$batch, nPCS = 7, softpower = 18)## Found 24380 genes with uniform expression within a single batch (all zeros); these will not be adjusted for batch.## Found3batches## Adjusting for0covariate(s) or covariate level(s)## Standardizing Data across genes## Fitting L/S model and finding priors## Finding parametric adjustments## Adjusting the Data## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at -2.4183## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 0.30103## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 9.8548e-16## PC_ 1
## Positive: ANK3, ZBTB20, PCAT1, FAAH2, TSHZ2, MAML2, ABLIM1, PRKCA, SESN3, TTN
## LDLRAD4, UST, MPP7, FMN1, AL353660.1, ACYP2, TAFA1, MGAT4A, AL136456.1, EPHA4
## Negative: TUBA1B, TUBB, HIST1H4C, RRM2, STMN1, DIAPH3, HIST1H3B, GAPDH, POLQ, ACTB
## HIST1H2AH, NCAPG, TK1, HIST1H1B, PFN1, TMPO, NUSAP1, PCLAF, SHCBP1, ASPM
## PC_ 2
## Positive: POLQ, NCAPG, SPC25, DIAPH3, CIT, KIF15, KIF18B, RRM2, ASPM, KIF11
## CDCA2, NCAPG2, ATAD5, NUSAP1, KIF14, KIF4A, TOP2A, BUB1B, KNL1, BRIP1
## Negative: RPL28, RPL18A, RPL41, RPL13A, RPS15, RPL27A, RPS19, RPL11, RPS12, RPS28
## RPS23, RPS29, RPS18, RPL10, RPL37, RPS27, RPS2, RPLP1, B2M, RPS16
## PC_ 3
## Positive: ABLIM1, MGAT5, RORA, MAP3K1, ZBTB20, SSBP2, WDFY2, MAP3K4, MCF2L2, ATP13A3
## AL136456.1, RFX3, ARL15, TSHZ2, PHF21A, STAM, DUSP16, AKT3, MAST4, UST
## Negative: MTRNR2L8, NKG7, AGAP1, CCL5, PECAM1, CD8A, MKI67, CENPF, GZMA, KLRK1
## HMGB2, TOP2A, CD8B, INCENP, KPNA2, AOAH, HIST1H1E, CENPE, CDCA2, DLGAP5
## PC_ 4
## Positive: PECAM1, PTPN3, DTHD1, MIR4422HG, LYST, MCTP2, NCOA1, GZMK, KLRK1, CNR2
## SESTD1, NKG7, AGAP1, AL031599.1, HAVCR2, KCNQ5, UBE2E2, SAMD3, GZMA, AFF3
## Negative: LTB, MAP3K1, SORL1, KIF14, KIF4A, CDCA2, CDCA8, UBE2C, KIF23, TSHZ2
## ANK3, CKAP2L, KIF2C, PLK1, KIF20A, CCNB2, ESPL1, CCNF, GTSE1, HMMR
## PC_ 5
## Positive: MIR4422HG, PTPN3, SESTD1, DTHD1, AFF3, GZMK, SESN3, AL031599.1, LDLRAD4, PECAM1
## UBE2E2, ANTXR2, BACH2, LYST, HAVCR2, KIF20A, PLK1, DSCAML1, PACSIN1, KIF14
## Negative: EXO1, DTL, FANCA, CLSPN, TRERF1, CCNE2, CDC6, MSH6, CDC45, CTSW
## MCM4, POLE2, FAM111B, HELLS, MCOLN2, CENPP, C1orf21, CCL5, MPP6, MCM10
## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.00209 0.443 0.1190 136.000 1.34e+02 151.00
## 2 2 0.23500 -2.250 0.3650 73.400 7.07e+01 94.00
## 3 3 0.53100 -2.360 0.5380 40.800 3.77e+01 61.60
## 4 4 0.64600 -2.090 0.5780 23.400 2.03e+01 42.40
## 5 5 0.66600 -1.850 0.5830 13.900 1.11e+01 30.40
## 6 6 0.60200 -1.900 0.5280 8.620 6.09e+00 22.60
## 7 7 0.60300 -1.620 0.5240 5.540 3.41e+00 17.20
## 8 8 0.62800 -1.480 0.5540 3.700 1.93e+00 13.40
## 9 9 0.66600 -1.390 0.6050 2.550 1.14e+00 10.60
## 10 10 0.67600 -1.310 0.6300 1.820 6.62e-01 8.50
## 11 12 0.21200 -2.540 0.1180 0.989 2.33e-01 5.59
## 12 14 0.20500 -2.180 0.1330 0.578 9.50e-02 3.79
## 13 16 0.21000 -2.100 0.1550 0.354 4.05e-02 2.64
## 14 18 0.80100 -1.090 0.8340 0.223 1.68e-02 1.86
## 15 20 0.19200 -1.870 0.0244 0.144 7.77e-03 1.33
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## dynamicMods
## 0 1 2 3 4
## 165 33 25 22 13
## [1] "4129/10000 permutations failed the Mann-Whitney test."
## [1] "0/10000 permutations failed the Mann-Whitney test."
## [1] "206/10000 permutations failed the Mann-Whitney test."
## [1] "0/10000 permutations failed the Mann-Whitney test."
## $turquoise
## [1] "TUBA1B" "TUBB" "STMN1" "DIAPH3" "POLQ" "NCAPG"
## [7] "HIST1H1B" "PCLAF" "ASPM" "KIF11" "NCAPG2" "TOP2A"
## [13] "CIT" "KIF15" "ATAD5" "KNL1" "CIP2A" "STIL"
## [19] "SSBP2" "CD226" "CENPF" "ANTXR2" "LTB" "DTL"
## [25] "FANCA" "HELLS" "WDR76" "FHIT" "ANXA2" "TGFBR3"
## [31] "MCM5" "MCM7" "POLA1"
##
## $yellow
## [1] "RRM2" "NUSAP1" "BRIP1" "MKI67" "FANCI" "ECT2" "MCF2L2" "CENPE"
## [9] "AFF3" "APOLD1" "CLSPN" "MCM4" "CENPP"
##
## $blue
## [1] "RPL28" "RPL18A" "RPL41" "RPL13A" "RPS15" "RPL27A" "RPS19" "RPL11"
## [9] "RPS12" "RPS28" "RPS23" "RPS29" "RPS18" "RPL10" "RPL37" "RPS27"
## [17] "RPS2" "RPLP1" "B2M" "RPS16" "RPL18" "RPL37A" "RPS27A" "RPLP2"
## [25] "RPS15A"devtools::session_info()## Warning in system("timedatectl", intern = TRUE): running command 'timedatectl'
## had status 1## - Session info ---------------------------------------------------------------
## setting value
## version R version 4.2.0 (2022-04-22)
## os Red Hat Enterprise Linux 8.7 (Ootpa)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate C
## ctype C
## tz Etc/UTC
## date 2023-06-01
## pandoc 2.17.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/ (via rmarkdown)
##
## - Packages -------------------------------------------------------------------
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## tensor 1.5 2012-05-05 [2] CRAN (R 4.2.0)
## tibble 3.2.1 2023-03-20 [1] CRAN (R 4.2.0)
## tidyr 1.3.0 2023-01-24 [1] CRAN (R 4.2.0)
## tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.0)
## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.0)
## usethis 2.1.6 2022-05-25 [1] CRAN (R 4.2.0)
## utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.0)
## uwot 0.1.14 2022-08-22 [1] CRAN (R 4.2.0)
## vctrs 0.6.2 2023-04-19 [1] CRAN (R 4.2.0)
## vipor 0.4.5 2017-03-22 [2] CRAN (R 4.2.0)
## viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.2.0)
## WGCNA * 1.72-1 2023-01-18 [1] CRAN (R 4.2.0)
## withr 2.5.0 2022-03-03 [2] CRAN (R 4.2.0)
## xfun 0.39 2023-04-20 [1] CRAN (R 4.2.0)
## XML 3.99-0.14 2023-03-19 [1] CRAN (R 4.2.0)
## xtable 1.8-4 2019-04-21 [2] CRAN (R 4.2.0)
## XVector 0.38.0 2022-11-01 [1] Bioconductor
## yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.0)
## zip 2.3.0 2023-04-17 [1] CRAN (R 4.2.0)
## zlibbioc 1.44.0 2022-11-01 [1] Bioconductor
## zoo 1.8-12 2023-04-13 [1] CRAN (R 4.2.0)
##
## [1] /gpfs/gibbs/project/ya-chi_ho/jac369/R/4.2
## [2] /vast/palmer/apps/avx2/software/R/4.2.0-foss-2020b/lib64/R/library
##
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